Data visualization in clinical practice

Abstract

As health-care data is increasingly digitized and standardized not only for research purposes but also for clinical practice, opportunities for increased personalized medicine through big data analytics arise. However, practical limitations exist towards acceptance of data analytics models to be used in clinical practice. Traditionally, models (typically rule-based) are extensively validated before being taken up in practice. With the fast pace of development of new data, techniques, and devices, time-consuming external validation will often invalidate future application of a model, due to new or better diagnostic measurements or treatment techniques. To accommodate for this fast pace of development, a more flexible way of model development is needed. This entails that certain levels of uncertainty need to be accepted in the external validity of the model, either because the model has not undergone thorough external validation or because circumstances have changed since the model was developed. We can allow for the doctor to stay in charge of any inferences made from data through visualization instead of mere presentation of, e.g., risk scores or survival probabilities from a trained model. Absence of external validation requires that visualizations are easily interpretable: it should be clear how they were constructed (they should be as unbiased as possible), and the limitations of the underlying model of the data should be clearly presented to the user. In this chapter, we present direct data visualization techniques, which adhere to these requirements, along with their limitations and directions for future research into readily interpretable, unbiased data visualizations for big data in health care.